Optimization of AI-Powered Edge-IoT Architecture for Real-Time Response in Distributed Smart City Systems - Amrullah, Ata (Intellithings Journal, 2024)
Smart city development, increasingly powered by the widespread adoption of Internet of Things (IoT) devices, demands systems capable of processing data in real time and with high reliability. Traditional cloud-based models often fall short due to latency, bandwidth issues, and privacy risks when managing the constant stream of data from distributed IoT sensors. This paper reviews recent advancements and proposes optimization strategies for integrating Artificial Intelligence (AI) into Edge-IoT systems, specifically designed to enhance responsiveness in smart city environments. Key areas include lightweight AI model design, adaptive resource management, efficient data flow, and network enhancements. We highlight technologies such as federated learning, task offloading, and software-defined networking to minimize delays and increase performance. In addition, the paper discusses challenges—scalability, heterogeneity, energy efficiency, and security—while outlining promising directions for future research. This work offers valuable insights for researchers and professionals working to build smart urban systems that are responsive, efficient, and context-aware.